获取评分次数超50的餐厅名称时遇TypeError错误求助

原代码：

# Filter the rated restaurants
df_rated = df[df['rating'] != 'Not given'].copy()

# Convert rating column from object to integer
df_rated['rating'] = df_rated['rating'].astype('int')  

# Create a dataframe that contains the restaurant names with their rating counts
df_rating_count = df_rated.groupby(['restaurant_name'])['rating'].count().sort_values(ascending = False).reset_index()
df_rating_count.head() 

# Get the restaurant names that have rating count more than 50
rest_names = df_rating_count[['rating']>50]['restaurant_name'] ## Complete the code to get the restaurant names having rating count more than 50
# Filter to get the data of restaurants that have rating count more than 50
df_mean_4 = df_rated[df_rated['restaurant_name'].isin(rest_names)].copy()

# Group the restaurant names with their ratings and find the mean rating of each restaurant
df_mean_4.groupby(['restaurant_name'])['rating'].mean().sort_values(ascending = False).reset_index().dropna() ## Complete the code to find the mean rating

运行报错：

TypeError                                 Traceback (most recent call last)
<ipython-input-46-9676daed1fbc> in <module>
      1 # Get the restaurant names that have rating count more than 50
----> 2 rest_names = df_rating_count[['rating']>50]['restaurant_name'] ## Complete the code to get the restaurant names having rating count more than 50
      3 # Filter to get the data of restaurants that have rating count more than 50
      4 df_mean_4 = df_rated[df_rated['restaurant_name'].isin(rest_names)].copy()
      5 

TypeError: '>' not supported between instances of 'list' and 'int'

错误原因 #

df_rating_count[['rating']]返回的是DataFrame类型（二维结构），无法直接与整数50做大小比较；而df_rating_count['rating']返回的是一维Series序列，可直接和整数做比较。

修正后的代码 #

# 过滤未评分餐厅
df_rated = df[df['rating'] != 'Not given'].copy()

# 将评分列转为整数类型
df_rated['rating'] = df_rated['rating'].astype('int')  

# 统计每家餐厅的评分次数并降序排序
df_rating_count = df_rated.groupby(['restaurant_name'])['rating'].count().sort_values(ascending=False).reset_index()
# 重命名计数列，提升可读性
df_rating_count.rename(columns={'rating': 'rating_count'}, inplace=True)

# 获取评分次数超过50的餐厅名称
rest_names = df_rating_count[df_rating_count['rating_count'] > 50]['restaurant_name']

# 筛选目标餐厅的评分数据
df_mean_4 = df_rated[df_rated['restaurant_name'].isin(rest_names)].copy()

# 计算平均评分并降序排序
result = df_mean_4.groupby(['restaurant_name'])['rating'].mean().sort_values(ascending=False).reset_index().dropna()
# 重命名均值列
result.rename(columns={'rating': 'average_rating'}, inplace=True)

print(result)

额外优化 #

可以合并步骤，减少中间变量，让逻辑更连贯高效：

# 一步完成：统计评分次数>50的餐厅，同时计算平均评分
result = df_rated.groupby('restaurant_name').agg(
    rating_count=('rating', 'count'),
    average_rating=('rating', 'mean')
).query('rating_count > 50').sort_values('average_rating', ascending=False).reset_index().dropna()

print(result)

内容的提问来源于stack exchange，提问作者MauricioG